Pattern recognition in honeybees: eidetic imagery and orientation discrimination

The roles of eidetic imagery and orientational cues, respectively, in the discrimination of visual patterns by honeybees (Apis mellifera) were evaluated by training the bees to discriminate between patterns consisting of periodic, black and white square wave gratings. Training and tests with a number of different pairs of patterns revealed that bees use orientational cues almost exclusively, if such are present, and make use of eidetic images only when orientational cues are not available. On the other hand, if a pattern carries strong orientational cues, bees learn the orientation even if it is irrelevant to the discrimination task on which they are trained.     

Visual marks learned by the solitary bee Megachile rotundata for localizing its nest

We studied homing behaviour of leaf-cutter bees, Megachile rotundata, by using artificial landmarks. We evaluated their nest-searching behaviour in different test situations to elucidate the nature of the visual marks they used in this task. When we modified or removed geometrical figures surrounding the nest, the bees searched for longer, showing that they noticed the introduced changes. However, these manipulations never prevented bees from finding their nest, suggesting that other visual cues were crucial in the task. Manipulations of the edges provided by the boundaries of the device (nest block, metal sheet on which the block was mounted) strongly impaired the homing performance. The further away the edges that were left intact, the stronger was the impairment of the homing behaviour. These results suggest that bees learn the distances of the various edges from the goal and that edges have a hierarchical significance according to their distance from the nest. The most distant edges provide vague information, which suffices to guide the insect towards the next edge in the sequence, until it recognizes the final, precise location of the nest. The results support the conclusion that information on distances is acquired using cues derived from motion parallax generated by the insect's self-motion. Recognition of edge parameters such as position and orientation might be achieved by an image-matching mechanism based on dynamic processes. Thus, in the homing task, there is no clear discrepancy between the eidetic and the parametric hypotheses of spatial representation.     

乌龟脑细胞辐射损伤的研究

在细胞培养的基础上,利用透射电镜和流式细胞术对乌龟脑细胞的辐射损伤进行了研究。在电镜下观察,未辐射脑细胞为不规则的几何形状,有突起伸出;细胞核不规则,几何状或椭圆形,核仁和核周间隙明显;线粒体基质电子密度均匀,内部嵴结构清晰;内质网清晰可见;细胞质匀质,内部充满大量糖原颗粒。乌龟脑细胞受辐射后,细胞外碎片增多;核出现明显的分叶,并可见核碎片;核染色质固缩、边集;线粒体、内质网和部分细胞质出现空泡化;可见凋亡小体。流式细胞术的检测结果显示:辐射组和未辐射组的细胞大部分都处于G₀/G₁期,分别占检测细胞总数59.7%和61.9%:在主峰前,辐射组细胞出现细胞凋亡峰:在G₂/M期,辐射组细胞的细胞数较未辐射组有所减少。研究结果表明,乌龟脑细胞经辐射后出现了凋亡现象。     

Le psychothérapeute psychanalyste « à <Emphasis Type="Italic">l’école de la phénoménologie</Emphasis> »

The mixing of psychoanalysis and phenomenology fosters the development of psychotherapy, psychoanalysis drawing on phenomenology to offer a method of studying and understanding the human being; this creates a genuine form of “anthropology”. Phenomenology’s contributions to psychoanalysis revolve around the three notions of épochè, the understanding of others and transference. On the one hand, épochè (a suspension of natural states of mind, obviousness and knowledge; an eidetic reduction; and an opening to the world and others) ensures better comprehension (“to take with yourself”) of others and their existential projects. On the other hand, épochè prevents falling into the transference and countertransference traps and sets the distance necessary for the hermeneutic act, that is, the interpretation of what is no longer there, what is absent, yet said and indicated by language in psychoanalytic therapy. Through its humanist, anthropological perspective and its overall understanding of the human being, phenomenology leads the way to rethinking clinical psychotherapy to create a more unified discipline.     

Microbiological database UNIQEM: problems and prospects

The UNIQEM database, designed to accumulate microbiological data of a general nature, is currently used to store and make available information about microorganisms studied and maintained at the Institute of Microbiology, Russian Academy of Sciences. UNIQEM is composed of two related databases. The first one is a catalogue database, used to accumulate and maintain list-form information on a wide range of microorganisms. The second one is a property database, which facilitates collecting, processing, and publishing diverse data having to do with these microorganisms and their properties. The database supports the retrieval of microorganisms by specifying an arbitrary set of their properties and has the potential for eventually evolving into a computer instrument for unattended identification of microorganisms.     

Classification, error detection, and reconciliation of process information in complex biochemical systems

Bioprocess identification starts with collection of process information. Usually there is a variety of information available, consisting of actual measurement data, historical data, empirical kinetic and yield correlations, and general knowledge available from literature. A central problem is to find out how the various pieces of information should be integrated. In addition, one should know how to deal with missing, inconsistent, or too inaccurate data. Recently, a general systematic method for dealing with these problems, based on conservation constraints, was published, and application shown to simple black box systems. In this article, the scope is generalized by including metabolic network data and dispersed process information of diverse type and nature, such as multiple sources of the value of one particular quantity, use of kinetic expressions, analytical problems, cometabolism or mixed substrate utilization, and chemical reactions. The alkalophilic bacterium Acinetobacter calcoaceticus is used as a model organism, growing on acetate and converting xylose into xylonolactone. It is shown that all relevant pieces of information can be straightforwardly and systematically treated, by considering them as constaints. In general, it is illustrated how the search for directed process improvements starts with an optimal selection of information sources, followed by an accurate analysis of possible metabolic bottlenecks. In this particular case it is shown that the yield of A. calcoaceticus on acetate at varying xylose/acetate feed ratios can be accurately predicted using dispersed process information. (c) 1996 John Wiley & Sons, Inc.     

MOsDB: an integrated information resource for rice genomics

The MIPS Rice (Oryza sativa) database (MOsDB; http://mips.gsf.de/proj/rice) provides a comprehensive data collection dedicated to the genome information of rice. Rice (O. sativa L.) is one of the most important food crops for over half the world's population and serves as a major model system in cereal genome research. MOsDB integrates data from two publicly available rice genomic sequences, O. sativa L. ssp. indica and O. sativa L. ssp. japonica. Besides regularly updated rice genome sequence information, MOsDB provides an integrated resource for associated analysis data, e.g. internal and external annotation information as well as a complex characterization of all annotated rice genes. The MOsDB web interface supports various search options and allows browsing the database content. MOsDB is continuously expanding to include an increasing range of data type and the growing amount of information on the rice genome.     

An image-matching mechanism describes a generalization task in honeybees

Currently two hypotheses exist as to how an insect's CNS stores and compares learned visual images: storage as a photograph-like eidetic image (template-hypothesis), and storage as a set of features extracted by the visual system (parameter-hypothesis). An important criterion for deciding between these two hypotheses is the generalization of a learned pattern to other patterns. We therefore trained bees to patterns presented on a vertical screen and to discriminate between either a black triangle and a ring-pattern (training A, B), or a black disc and an outline triangle (training C), and then tested for a possible generalization of the learned patterns to a variety of other patterns. Several results obtained that way (e.g. the transfer test, column 2 in Fig. 1a, or Fig. 3) seem to indicate that the bees indeed do generalize a learned pattern to other patterns, and hence appear to support the parameter-hypothesis. Quite unexpectedly, however, it turned out that the same results can also be explained by a template matching mechanism. In simulating an image matching strategy we used a similarity function that had been successfully applied to pattern discrimination data obtained in tetheredly flying fruitflies, Drosophila. This similarity function assumes that the insect evaluates the ratio between the overlap of an actual retinal image with the stored template and the total area of the actual retinal image (see Fig. 5). It is able to describe the bulk of our data. Interestingly, the training situation appears to have an influence on the weight attributed by the bee to different parts of the template. Such a situation-dependent weighting might indicate a first stage of parameter extraction.Abbreviations TR + positive training shape - TR negative training shape     

Open source system for analyzing, validating, and storing protein identification data

This paper describes an open-source system for analyzing, storing, and validating proteomics information derived from tandem mass spectrometry. It is based on a combination of data analysis servers, a user interface, and a relational database. The database was designed to store the minimum amount of information necessary to search and retrieve data obtained from the publicly available data analysis servers. Collectively, this system was referred to as the Global Proteome Machine (GPM). The components of the system have been made available as open source development projects. A publicly available system has been established, comprised of a group of data analysis servers and one main database server.     

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

ABSTRACT: BACKGROUND: An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. RESULTS: We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. CONCLUSIONS: The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge.     

An intelligent biological information management system

MOTIVATION: As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area. RESULTS: BioSifter was tested using 500 documents obtained from PubMed database on two biological problems related to genetic polymorphism and extracorporal shockwave lithotripsy. The results indicate that BioSifter is a powerful tool for biological researchers to automatically retrieve relevant text documents from biological literature based on their interest profile. The results also indicate that the first stage of information management process, i.e. data to information transformation, significantly reduces the size of the information space. The filtered data obtained through BioSifter is relevant as well as much smaller in dimension compared to all the retrieved data. This would in turn significantly reduce the complexity associated with the next level transformation, i.e. information to knowledge.     

Cluster analysis of comparative genomic hybridization (CGH) data using self-organizing maps: application to prostate carcinomas.

Comparative genomic hybridization (CGH) is a modern genetic method which enables a genome-wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more) metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20-30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self-organizing map (Genecluster) as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self-organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data). We studied a group of 40 recent cases without follow-up, an older group of 20 cases with follow-up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance.     

geophylobuilder 1.0: an arcgis extension for creating 'geophylogenies'

Evolution is inherently a spatiotemporal process; however, despite this, phylogenetic and geographical data and models remain largely isolated from one another. Geographical information systems provide a ready-made spatial modelling, analysis and dissemination environment within which phylogenetic models can be explicitly linked with their associated spatial data and subsequently integrated with other georeferenced data sets describing the biotic and abiotic environment. geophylobuilder 1.0 is an extension for the arcgis geographical information system that builds a 'geophylogenetic' data model from a phylogenetic tree and associated geographical data. Geophylogenetic database objects can subsequently be queried, spatially analysed and visualized in both 2D and 3D within a geographical information systems.     

Automatic Diagnosis of Pathological Myopia from Heterogeneous Biomedical Data

Pathological myopia is one of the leading causes of blindness worldwide. The condition is particularly prevalent in Asia. Unlike myopia, pathological myopia is accompanied by degenerative changes in the retina, which if left untreated can lead to irrecoverable vision loss. The accurate diagnosis of pathological myopia will enable timely intervention and facilitate better disease management to slow down the progression of the disease. Current methods of assessment typically consider only one type of data, such as that from retinal imaging. However, different kinds of data, including that of genetic, demographic and clinical information, may contain different and independent information, which can provide different perspectives on the visually observable, genetic or environmental mechanisms for the disease. The combination of these potentially complementary pieces of information can enhance the understanding of the disease, providing a holistic appreciation of the multiple risks factors as well as improving the detection outcomes. In this study, we propose a computer-aided diagnosis framework for Pathological Myopia diagnosis through Biomedical and Image Informatics(PM-BMII). Through the use of multiple kernel learning (MKL) methods, PM-BMII intelligently fuses heterogeneous biomedical information to improve the accuracy of disease diagnosis. Data from 2,258 subjects of a population-based study, in which demographic and clinical information, retinal fundus imaging data and genotyping data were collected, are used to evaluate the proposed framework. The experimental results show that PM-BMII achieves an AUC of 0.888, outperforming the detection results from the use of demographic and clinical information 0.607 (increase , ), genotyping data 0.774 (increase , ) or imaging data 0.852 (increase , ) alone. The accuracy of the results obtained demonstrates the feasibility of using heterogeneous data for improved disease diagnosis through our proposed PM-BMII framework.     

Comprehensive approach to analyzing rare genetic variants

Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic "step-up" approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.     

Proposed standard for image cytometry data files

A number of different types of computers running a variety of operating systems are presently used for the collection and analysis of image cytometry data. In order to facilitate the development of sharable data analysis programs, to allow for the transport of image cytometry data from one installation to another, and to provide a uniform and controlled means for including textual information in data files, this document describes a data storage format that is proposed as a standard for use in image cytometry. In this standard, data from an image measurement are stored in a minimum of two files. One file is written in ASCII to include information about the way the image data are written and optionally, information about the sample, experiment, equipment, etc. The image data are written separately into a binary file. This standard is proposed with the intention that it will be used internationally for the storage and handling of biomedical image cytometry data. The method of data storage described in this paper is similar to those methods published in American Association of Physicists in Medicine (AAPM) Report Number 10 and in ACR-NEMA Standards Publication Number 300-1985.     

Some Considerations on Data Integration for the Next-Generation Flora (iFlora) and Flora Revision—— A Case Study of Gaultheria (Ericaceae)

The next-generation intelligent Flora (iFlora) is designed to integrate current botanical knowledge, with molecular biology information and computer technology. The most important and urgent task for iFlora development is to search for an approach to incorporate all useful data into an accurate, most up-to-date and complete information database for a taxon, and hierarchically classify these data to meet different demands from iFlora users, to provide the user an authentic, scientific research based platform for sharing botanic knowledge, and associated valuable information for the benefit of national economy and quality of our life. In this study, we summarized the innovations, hierarchical classifications and functions of data for iFlora, in contrast of that of the previous Floras and the frequently used digital plant databases or eFloras. The innovation and essential of data compilation and integration of the iFlora was emphasized as intelligent assembly of data from DNA barcodes, key morphological characters, digital images and molecular phylogenetics with the support of computer techniques to achieve intelligent plant identification. We attempted to integrate previously assembled research data into the iFlora, and list three hierarchically classified data and their functions, and related issues, with the genus Gaultheria and one of its species, G.hookeri, as test case.     

iFlora与植物志修订的若干思考——以杜鹃花科白珠树属为例

结合现代植物学、DNA测序与信息等关键技术而产生的新一代智能植物志（iFlora）,其研发中最首要和迫切的任务之一就是如何将前沿、准确和完善的植物数据信息进行特色整合及智能化处理,为用户提供一个客观而科学的,具理论和实际应用为一体的植物学知识共享平台,并有效地为国民经济发展提供有价值的植物资源信息渠道。本文简要介绍了与传统植物志和目前常用的电子植物志数据库相区别的iFlora数据信息的分级内容、特点和功能,并强调了作为iFlora的核心数据信息,即用于物种鉴定的植物DNA条形码、关键形态学分类特征、植物图像等识别数据,以及分子系统发育数据等。以杜鹃花科（Ericaceae）白珠树属（Gaultheria）和其属下红粉白珠（G.hookeri）为例,介绍了iFlora采用的三类数据（核心数据、基础数据和拓展数据）构成的三级信息及其功能,同时探讨了信息整合时可能遇到的问题。     

Cell sociology and the problem of automation in the development of pluricellular animals

The principles of automation (automatism and programming) in the unfolding of spatio-temporal patterns during animal development are deduced from experimental data reconsidered from the point of view of cell sociology. The developmental programme in the egg is not part of the genetic information but a part of the cytoplasmic information. Throughout development cells store extra-cellular information released by their neighbours in the form of cytoplasmic information. Successive determinations cannot be considered as successive reprogrammings of cells: each one consists of a selection of one specific programme from the total information previously stored. This programme specifies cell interactions in the determined population as a whole; it is very imprecise and is progressively completed during the course of further differentiation by information released by neighbouring cell populations. Complicated patterns may emerge from only two homogeneous populations involved in distinct differentiation pathways and confronting each other. Consequently the egg developmental programme provides gene effectors and specific physico-chemical conditions necessary for the starting of at least two distinct differentation pathways. Experimental data suggest that there are two components in this programme. One is a molecular machinery which starts at fertilization in the whole cytoplasm. It yields two programmes of differentiation, typically first an endodermal and then an ectodermal one. The other component of the egg developmental programme, which does not require specific information, allows the interception of the first (endodermal) programme. The application of informatics to developmental automatism is discussed in the latter part of the paper.